DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-8 are pending and examined herein per Applicant’s 02/28/2025 filing with the USPTO.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/28/2025 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 150A3, 150A4, and 150A5 (see Sepc. [42]). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 150A (see Fig. 3). Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nath et al (US 2015/0317582 A1).
Claim 1
Nath teaches worker evaluation system comprising (Nath [24] “ the system diagram of FIG. 1 illustrates the interrelationships between program modules for implementing various embodiments of the Context-Aware Crowdsourced Task Optimizer”):
a calculation unit (Nath [150] “portable electronic devices, wearable computing devices, hand-held computing devices, laptop or mobile computers, communications devices such as cell phones, smartphones and PDA's, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, audio or video media players, handheld remote control devices, etc”);
an input/output unit (Nath [153] “conventional computer input devices 540 or combinations of such devices (e.g., touchscreens, touch-sensitive surfaces, pointing devices, keyboards, audio input devices, voice or speech-based input and control devices, video input devices, haptic input devices, devices for receiving wired or wireless data transmissions, etc.) . . . conventional computer input devices 540 or combinations of such devices (e.g., touchscreens, touch-sensitive surfaces, pointing devices, keyboards, audio input devices, voice or speech-based input and control devices, video input devices, haptic input devices, devices for receiving wired or wireless data transmissions, etc.)”); and
a storage unit, wherein the storage unit stores (Nath [157] “any desired combination of computer or machine readable media or storage devices”):
worker actual working information in which a worker, a specific object, and specific object information that is information regarding a specific object related to a worker are associated (Nath [88] “a wide range of additional considerations or parameters (e.g., age, gender, fitness level, education, skills, worker's computing devices, tools, equipment, travel capabilities, quality reviews of worker or task result from task publisher, etc.)”);
overall performance information regarding overall performance of works of workers for the specific object (Nath [15] “worker history, present or expected worker locations, travel paths, working hours, skill set, capabilities of worker's fixed and mobile computing devices, etc.” and [16] “ machine learning techniques consider each workers history and behavior with respect to task types, task completion rates, contexts such as locations, travel direction, schedule, capabilities or skills of each worker (e.g., professional photographer, foreign language skills, etc.), capabilities of the workers mobile computing devices or tools (e.g., high resolution still or video cameras, laser rangefinders, microphones, etc.)”); and
classification information that is information regarding a classification to which the specific object belongs (Nath [18] “groups of human and/or virtual workers can also be “bundled” by the Context-Aware Crowdsourced Task Optimizer such that multiple workers can cooperate to perform particular tasks or task bundles. In the case of multiple workers acting as a group”),
the input/output unit accepts input of worker specification information that specifies a worker to be analyzed (Nath [30] “user input to determine and update the current and future worker contexts” and [50] “receives inputs including historical, real-time and future context information of multiple workers, and task properties (such as location, payment, deadline, etc.) from one or more task publishers. Given these inputs, the Context-Aware Crowdsourced Task Optimizer outputs recommended assignments of bundles of tasks to active workers.”),
the calculation unit specifies specific object information of the worker based on the worker specification information and the worker actual working information, calculates individual performance information that indicates performance of a work of the worker regarding the specific object based on the specific object information, compares the individual performance information and the overall performance information to calculate individual evaluation information that indicates evaluation of the worker for the specific object (Nath [52] “evaluate the learned worker models to return a probabilistic likelihood that particular workers will perform the particular task or bundle (optionally within some period of time).” And [85] “relative metric that can be evaluated in a variety of ways that may depend upon a workers skills, computing devices, tools, travel capabilities, etc.”), and
specifies a similar specific object belonging to a classification similar to the classification to which the specific object belongs or a similar process of a specific object belonging to a classification similar to the classification to which the specific object belongs, based on classification information (Nath [106] “each worker, there is an exponential number of different suitable task sets for which g.sub.ω(.) can be evaluated.”), and
the input/output unit outputs the individual evaluation information and the similar specific object or the similar process (Nath [57] “outputs recommended assignments of bundles of tasks to active workers”).
Claim 2
Nath teaches all the limitations of the worker evaluation system according to claim 1, wherein the similar process is a process in the same classification as the classification to which the specific object belongs or a process in a classification incorporating the classification to which the specific object belongs (Nath [52] and [62]).
Claim 3
Nath teaches all the limitations of the worker evaluation system according to claim 1, wherein the individual evaluation information includes information representing a degree of recommendation that recommends to allocate or not to allocate the worker to the similar process (Nath [16] and [68]), and
the degree of recommendation is a degree in accordance with the calculated evaluation of the worker (Nath [16] and [68]).
Claim 4
Nath teaches all the limitations of the worker evaluation system according to claim 3, wherein
in a case where the determined evaluation of the worker is relatively high, the degree of recommendation is a degree of how recommendable it is to allocate the worker to the similar process (Nath [16] and [68]), and
in a case where the determined evaluation of the worker is relatively low, the degree of recommendation is a degree of how recommendable it is not to allocate the worker to the similar process (Nash [72-73]).
Claim 5
Nath teaches all the limitations of the worker evaluation system according to claim 1, wherein the worker is a worker evaluated in advance (Nath [18]), and
the individual evaluation information includes information representing evaluation performed in advance, the evaluation factor, and the evaluation determined for the evaluation factor (Nath [30]).
Claim 6
Nath teaches all the limitations of the worker evaluation system according to claim 1, wherein
the calculation unit calculates the individual evaluation information for a plurality of specific objects (Nath [30] and [80]),
the input/output unit accepts designation of a specific object to which the worker assigns high priority (Nath [75]), and
the calculation unit extracts a specific object or a process in a classification similar to a classification to which a specific object designated as having relatively high priority belongs among specific objects to which relatively high individual evaluation is provided (Nath [50]).
Claim 7
Nath teaches a worker evaluation method to be performed by a computer, the worker evaluation method comprising (Nath [34]):
accepting input of worker specification information that specifies a worker to be analyzed (Nath [30] and [50]);
specifying specific object information of the worker based on worker specification information and worker actual working information (Nash [18]);
the worker actual working information being information in which a worker, a specific object, and specific object information that is information regarding a specific object related to a worker are associated (Nash [88]),
calculating individual performance information that indicates performance of a work of the worker regarding the specific object based on the specific object information (Nash [52] and [85]);
comparing the individual performance information and overall performance information to calculate individual evaluation information that indicates evaluation of the worker for the specific object (Nash [52] and [85]);
the overall performance information being information regarding overall performance of works of workers for the specific object (Nash [52] and [85]),
specifying a similar specific object belonging to a classification similar to a classification to which the specific object belongs or a similar process of a specific object belonging to a classification similar to the classification to which the specific object belongs, based on classification information that is information regarding the classification to which the specific object belongs (Nash [106]); and
outputting the individual evaluation information and the similar specific object or the similar process (Nash [57]).
Claim 8
Nath teaches a recording medium recording a computer program that causes a computer to execute (Nath [154-155]):
accepting input of worker specification information that specifies a worker to be analyzed (Nath [88]);
specifying specific object information of the worker based on worker specification information and worker actual working information (Nath [88]);
the worker actual working information being information in which a worker, a specific object, and specific object information that is information regarding a specific object related to a worker are associated (Nath [88]),
calculating individual performance information that indicates performance of a work of the worker for the specific object based on the specific object information (Nath [52] and [85]);
comparing the individual performance information and overall performance information to calculate individual evaluation information that indicates evaluation of the worker for the specific object, the overall performance information being information regarding overall performance of works of workers for the specific object (Nath [52] and [85]),
specifying a similar specific object belonging to a classification similar to a classification to which the specific object belongs or a similar process of a specific object belonging to a classification similar to the classification to which the specific object belongs, based on classification information that is information regarding the classification to which the specific object belongs (Nath [106]); and
outputting the individual evaluation information and the similar specific object or the similar process (Nath [57]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tsutsumi et al (US 2021/0097884 A1) teaches work support apparatus includes a storage unit that stores work instruction information including instructions of work procedures for instructing workers to perform predetermined work processes and work capacity model information including a plurality of models determined based on physical characteristics of the workers in respective work processes; a work sensing processing unit that senses the physical characteristic of the worker during a work that the worker performs based on the work instruction information and generates sensing information; and a work capacity evaluation processing unit that selects, for each of the workers, one or a plurality of models corresponding to the sensing information from the work capacity model information and associates the selected model with the sensing information to generate work capacity evaluation information.
Matsunaga (US 2021/0166180 A1) teaches prediction model may output, for example, a production amount on a work line in a case where work is performed by a plurality of workers input through the input unit 41. In such a prediction model, once workers who perform the work are input, a predicted production amount on the work line is output. Specifically, a prediction result indicating that a production amount on the work line is 1100/h when workers A, D, and E perform work is output. With such a prediction model, a change of a combination of workers is performed, and a combination of workers that can achieve the highest production amount is searched for.
Burn et al (US 11,232,383 B1) teaches GUI is configured to receive an assignment input in real-time, wherein the assignment input includes at least one new assignment for the management team and/or the startup company's and/or plurality of startup companies' employees to improve the startup company's and the plurality of startup companies' performance, wherein the artificial intelligence engine is configured to continuously receive, monitor and model all data received and outputted by the system.
Kratzer et al (US 2022/0292999 A1) teaches the task is performed, performance data may be collected and used by the ML model for future recommendations for the particular worker and for other workers.
Buchbinder (US 2022/0374814 A1) teaches a machine learning models may be applied to generate an applicant match score that matches jobs posted by an employer to the likelihood of success of community members (people and digital assistants) for that job based on 1) key meta-data related to the past profile (Q-Scores for particular skills and tasks) and current engagement of the members, 2) apply natural language processing (NLP) to extract key requirements from job descriptions posted by employers on the community, and 3) using supervised learning methods to draw correlations between data from #1, #2 to generate an job/member match score.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FOLASHADE ANDERSON whose telephone number is (571)270-3331. The examiner can normally be reached Monday to Thursday 12:00 P.M. to 6:00 P.M. CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FOLASHADE ANDERSON/ Primary Examiner, Art Unit 3623
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